373 citations · 705 across the 14 of their papers we have counts for
14 papers
Improving Classifier Robustness through Active Generation of Pairwise Counterfactuals
Ananth Balashankar, Xuezhi Wang, Yao Qin +5
Counterfactual Data Augmentation (CDA) is a commonly used technique for improving robustness in natural language classifiers. However, one fundamental challenge is how to discover…
Towards Robust Prompts on Vision-Language Models
Jindong Gu, Ahmad Beirami, Xuezhi Wang +3
With the advent of vision-language models (VLMs) that can perform in-context and prompt-based learning, how can we design prompting approaches that robustly generalize to distribut…
Training Deep Boltzmann Networks with Sparse Ising Machines
Shaila Niazi, Navid Anjum Aadit, Masoud Mohseni +3
The slowing down of Moore's law has driven the development of unconventional computing paradigms, such as specialized Ising machines tailored to solve combinatorial optimization pr…
What Are Effective Labels for Augmented Data? Improving Calibration and Robustness with AutoLabel
Yao Qin, Xuezhi Wang, Balaji Lakshminarayanan +2
A wide breadth of research has devised data augmentation approaches that can improve both accuracy and generalization performance for neural networks. However, augmented data can e…
Effective Robustness against Natural Distribution Shifts for Models with Different Training Data
Zhouxing Shi, Nicholas Carlini, Ananth Balashankar +4
"Effective robustness" measures the extra out-of-distribution (OOD) robustness beyond what can be predicted from the in-distribution (ID) performance. Existing effective robustness…
Investigating Ensemble Methods for Model Robustness Improvement of Text Classifiers
Jieyu Zhao, Xuezhi Wang, Yao Qin +2
Large pre-trained language models have shown remarkable performance over the past few years. These models, however, sometimes learn superficial features from the dataset and cannot…
Are Vision Transformers Robust to Patch Perturbations?
Jindong Gu, Volker Tresp, Yao Qin
Recent advances in Vision Transformer (ViT) have demonstrated its impressive performance in image classification, which makes it a promising alternative to Convolutional Neural Net…
Understanding and Improving Robustness of Vision Transformers through Patch-based Negative Augmentation
Yao Qin, Chiyuan Zhang, Ting Chen +3
We investigate the robustness of vision transformers (ViTs) through the lens of their special patch-based architectural structure, i.e., they process an image as a sequence of imag…
CAT-Gen: Improving Robustness in NLP Models via Controlled Adversarial Text Generation
Tianlu Wang, Xuezhi Wang, Yao Qin +5
NLP models are shown to suffer from robustness issues, i.e., a model's prediction can be easily changed under small perturbations to the input. In this work, we present a Controlle…
Improving Calibration through the Relationship with Adversarial Robustness
Yao Qin, Xuezhi Wang, Alex Beutel +1
Neural networks lack adversarial robustness, i.e., they are vulnerable to adversarial examples that through small perturbations to inputs cause incorrect predictions. Further, trus…
Deflecting Adversarial Attacks
Yao Qin, Nicholas Frosst, Colin Raffel +2
There has been an ongoing cycle where stronger defenses against adversarial attacks are subsequently broken by a more advanced defense-aware attack. We present a new approach towar…
Detecting and Diagnosing Adversarial Images with Class-Conditional Capsule Reconstructions
Yao Qin, Nicholas Frosst, Sara Sabour +3
Adversarial examples raise questions about whether neural network models are sensitive to the same visual features as humans. In this paper, we first detect adversarial examples or…
Imperceptible, Robust, and Targeted Adversarial Examples for Automatic Speech Recognition
Yao Qin, Nicholas Carlini, Ian Goodfellow +2
Adversarial examples are inputs to machine learning models designed by an adversary to cause an incorrect output. So far, adversarial examples have been studied most extensively in…
A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction
Yao Qin, Dongjin Song, Haifeng Chen +3
The Nonlinear autoregressive exogenous (NARX) model, which predicts the current value of a time series based upon its previous values as well as the current and past values of mult…